A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Zijian Liu, Pinjia Zhang, Shan He and Jin Huang
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Zijian Liu: Key Laboratory of Vehicle Transmission, China North Vehicle Research Institute, Beijing 100072, China
Pinjia Zhang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Shan He: Department of Energy Technology, Aalborg University, DK-9220 Aalborg East, Denmark
Jin Huang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2021, vol. 14, issue 14, 1-21

Abstract: Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research.

Keywords: fault diagnosis; rotor; eccentricity; electric machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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